ALIF EXPLORATION
Selected Variables
base: Code of the patient
covariates:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Previous surgery - LEV
- LGap
- RLL
- Cobb LS curve (Degree)
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
outcomes_ql:
- 2Y. ODI - Score (%)
- 2Y. SRS22 - SRS Subtotal score
- 2Y. SF36 - MCS
- 2Y. SF36 - PCS
outcomes_radiology:
- 6W. Major curve Cobb angle
- 1Y. Major curve Cobb angle
- 6W. T1 Sagittal Tilt
- 1Y. T1 Sagittal Tilt
- 6W. Sagittal Balance
- 1Y. Sagittal Balance
- 6W. Global Tilt
- 1Y. Global Tilt
- 6W. Lordosis (top of L1-S1)
- 1Y. Lordosis (top of L1-S1)
- 6W. LGap
- 1Y. LGap
- 6W. Pelvic Tilt
- 1Y. Pelvic Tilt
- 6W. RSA
- 1Y. RSA
- 6W. RPV
- 1Y. RPV
- 6W. RLL
- 1Y. RLL
predictive:
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Osteotomy
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Tobacco use_First Visit
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
demographic:
- Age
- Gender
- Prior Spine Surgery
- ASA classification
- 3CO
- BMI_First Visit
- Global Tilt
- ideal LL
- Lordosis (top of L1-S1)
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
expanded:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Previous surgery - LEV
- LGap
- RLL
- Cobb LS curve (Degree)
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
- SRS22 - SRS Subtotal score_First Visit
- T1 Sagittal Tilt
- Sagittal Balance
- Global Tilt
- Lordosis (top of L1-S1)
- Pelvic Tilt
- RSA
- RPVPropensity Scores Common Support
Model Stats
- Treatment proportion: 0.123
- Model Type: elastic_net
- Accuracy: 0.8946931
- Params: alpha: 0.3307692 lambda: 0.010492
Average Treatment Effects - Radiology
Outcome: 6W. Major curve Cobb angle
Distribution:
0% 25% 50% 75% 100%
-72.0000 -20.0000 -10.2200 -3.0325 27.5500
Model Type Y: boosting
RMSE: 16.7497293841677
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
Model Type No: boosting
RMSE: 12.9493946872086
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -0.214 (Std.Error: 4.534)
Trimmed ATE (Yes-No): 0.183 (Std.Error: 4.702)
Upper ATE (Yes-No): -11.407 (Std.Error: 3.682)
Observational differences in treatment 2.425 (Yes-No)
treatment outcome
1: Yes 22.89275
2: No 20.46777`geom_smooth()` using method = 'loess' and formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. Major curve Cobb angle
Distribution:
0% 25% 50% 75% 100%
-64.00 -22.00 -9.00 -1.45 22.44
Model Type Y: boosting
RMSE: 18.7947717542362
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625
Model Type No: boosting
RMSE: 14.4723639222115
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.875
ATE (Yes-No): 3.627 (Std.Error: 3.451)
Trimmed ATE (Yes-No): 4.159 (Std.Error: 3.552)
Upper ATE (Yes-No): -10.07 (Std.Error: 4.908)
Observational differences in treatment 3.108 (Yes-No)
treatment outcome
1: Yes 23.52800
2: No 20.42007`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 6W. T1 Sagittal Tilt
Distribution:
0% 25% 50% 75% 100%
-23.631420 -6.000000 -1.457698 1.634417 18.000000
Model Type Y: boosting
RMSE: 5.25369515881882
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.875
Model Type No: boosting
RMSE: 6.09637117511365
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -6.089 (Std.Error: 1.11)
Trimmed ATE (Yes-No): -6.216 (Std.Error: 1.124)
Upper ATE (Yes-No): -2.368 (Std.Error: 4.465)
Observational differences in treatment -0.963 (Yes-No)
treatment outcome
1: Yes -3.692905
2: No -2.730217`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. T1 Sagittal Tilt
Distribution:
0% 25% 50% 75% 100%
-30.098675 -5.293893 -2.000000 1.311482 20.000000
Model Type Y: boosting
RMSE: 6.36184902743723
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75
Model Type No: boosting
RMSE: 5.87627142648511
Params: nrounds: 100.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625
ATE (Yes-No): -3.366 (Std.Error: 1.797)
Trimmed ATE (Yes-No): -3.34 (Std.Error: 1.859)
Upper ATE (Yes-No): -3.922 (Std.Error: 2.928)
Observational differences in treatment 0.373 (Yes-No)
treatment outcome
1: Yes -2.211871
2: No -2.585074`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 6W. Sagittal Balance
Distribution:
0% 25% 50% 75% 100%
-194.790 -68.985 -28.930 0.950 114.150
Model Type Y: boosting
RMSE: 52.5101740607718
Params: nrounds: 100.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5
Model Type No: boosting
RMSE: 52.7794685433082
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75
ATE (Yes-No): -48.206 (Std.Error: 12.698)
Trimmed ATE (Yes-No): -50.109 (Std.Error: 13.103)
Upper ATE (Yes-No): -3.191 (Std.Error: 28.381)
Observational differences in treatment -7.524 (Yes-No)
treatment outcome
1: Yes 25.28385
2: No 32.80798`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. Sagittal Balance
Distribution:
0% 25% 50% 75% 100%
-237.4700 -62.5225 -28.1650 6.4125 109.5400
Model Type Y: boosting
RMSE: 50.3091976370015
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
Model Type No: boosting
RMSE: 51.1389029817983
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.875
ATE (Yes-No): -39.274 (Std.Error: 10.766)
Trimmed ATE (Yes-No): -38.727 (Std.Error: 11.142)
Upper ATE (Yes-No): -50.941 (Std.Error: 22.87)
Observational differences in treatment -0.815 (Yes-No)
treatment outcome
1: Yes 37.53697
2: No 38.35226`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 6W. Global Tilt
Distribution:
0% 25% 50% 75% 100%
-68.6200 -18.0175 -6.0900 1.6025 16.0000
Model Type Y: boosting
RMSE: 14.6475670600561
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75
Model Type No: boosting
RMSE: 12.3318452107063
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -9.385 (Std.Error: 3.961)
Trimmed ATE (Yes-No): -9.422 (Std.Error: 4.131)
Upper ATE (Yes-No): -8.404 (Std.Error: 6.127)
Observational differences in treatment -5.198 (Yes-No)
treatment outcome
1: Yes 19.00525
2: No 24.20356`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. Global Tilt
Distribution:
0% 25% 50% 75% 100%
-62.630 -15.825 -5.100 1.000 26.000
Model Type Y: boosting
RMSE: 15.3980734089774
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
Model Type No: boosting
RMSE: 11.4132822257368
Params: nrounds: 100.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5
ATE (Yes-No): -14.692 (Std.Error: 3.624)
Trimmed ATE (Yes-No): -14.814 (Std.Error: 3.73)
Upper ATE (Yes-No): -11.834 (Std.Error: 7.127)
Observational differences in treatment -3.243 (Yes-No)
treatment outcome
1: Yes 22.58559
2: No 25.82885`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 6W. Lordosis (top of L1-S1)
Distribution:
0% 25% 50% 75% 100%
-71.000 -24.000 -9.635 0.000 44.110
Model Type Y: boosting
RMSE: 17.4592131441212
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75
Model Type No: boosting
RMSE: 15.4615325057658
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -5.565 (Std.Error: 4.915)
Trimmed ATE (Yes-No): -5.675 (Std.Error: 5.081)
Upper ATE (Yes-No): -2.454 (Std.Error: 5.899)
Observational differences in treatment -1.453 (Yes-No)
treatment outcome
1: Yes -50.61275
2: No -49.15926`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. Lordosis (top of L1-S1)
Distribution:
0% 25% 50% 75% 100%
-67.870 -24.405 -8.000 0.100 23.380
Model Type Y: boosting
RMSE: 23.0184897669382
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5
Model Type No: boosting
RMSE: 14.8712618456256
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625
ATE (Yes-No): -10.625 (Std.Error: 4.324)
Trimmed ATE (Yes-No): -10.742 (Std.Error: 4.459)
Upper ATE (Yes-No): -7.643 (Std.Error: 9.049)
Observational differences in treatment 1.798 (Yes-No)
treatment outcome
1: Yes -47.25676
2: No -49.05490`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 6W. LGap
Distribution:
0% 25% 50% 75% 100%
-71.0000 -24.0000 -9.2566 0.2107 49.5964
Model Type Y: boosting
RMSE: 18.4922462596739
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5
Model Type No: boosting
RMSE: 15.5952926002781
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75
ATE (Yes-No): -4.758 (Std.Error: 5.135)
Trimmed ATE (Yes-No): -4.886 (Std.Error: 5.322)
Upper ATE (Yes-No): -1.179 (Std.Error: 6.006)
Observational differences in treatment -2.381 (Yes-No)
treatment outcome
1: Yes 11.22173
2: No 13.60306`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. LGap
Distribution:
0% 25% 50% 75% 100%
-67.72420 -24.48880 -7.14660 0.68115 22.08000
Model Type Y: boosting
RMSE: 20.4487816321054
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75
Model Type No: boosting
RMSE: 14.9853997912591
Params: nrounds: 100.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75
ATE (Yes-No): -11.134 (Std.Error: 5.689)
Trimmed ATE (Yes-No): -11.225 (Std.Error: 5.92)
Upper ATE (Yes-No): -8.84 (Std.Error: 8.275)
Observational differences in treatment -0.197 (Yes-No)
treatment outcome
1: Yes 13.84335
2: No 14.04012`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 6W. Pelvic Tilt
Distribution:
0% 25% 50% 75% 100%
-36.4100 -8.8325 -2.0750 2.1025 14.4200
Model Type Y: boosting
RMSE: 11.2204976403802
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
Model Type No: boosting
RMSE: 7.62365220568526
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5
ATE (Yes-No): -3.843 (Std.Error: 2.492)
Trimmed ATE (Yes-No): -3.805 (Std.Error: 2.563)
Upper ATE (Yes-No): -4.991 (Std.Error: 3.756)
Observational differences in treatment -3.908 (Yes-No)
treatment outcome
1: Yes 18.08051
2: No 21.98834`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. Pelvic Tilt
Distribution:
0% 25% 50% 75% 100%
-26.6200 -7.0000 -2.0150 1.7425 23.0000
Model Type Y: boosting
RMSE: 9.94756705688136
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
Model Type No: boosting
RMSE: 6.82312808674904
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5
ATE (Yes-No): -8.568 (Std.Error: 1.817)
Trimmed ATE (Yes-No): -8.829 (Std.Error: 1.875)
Upper ATE (Yes-No): -1.976 (Std.Error: 3.988)
Observational differences in treatment -3.237 (Yes-No)
treatment outcome
1: Yes 19.46765
2: No 22.70514`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 6W. RSA
Distribution:
0% 25% 50% 75% 100%
-67.5592 -17.7592 -6.0986 1.8393 15.3720
Model Type Y: boosting
RMSE: 14.2396934972892
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625
Model Type No: boosting
RMSE: 11.955728277692
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -9.238 (Std.Error: 3.357)
Trimmed ATE (Yes-No): -9.29 (Std.Error: 3.473)
Upper ATE (Yes-No): -7.852 (Std.Error: 5.493)
Observational differences in treatment -4.264 (Yes-No)
treatment outcome
1: Yes 7.98349
2: No 12.24719`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. RSA
Distribution:
0% 25% 50% 75% 100%
-62.4716 -16.0000 -5.2168 1.0000 25.0400
Model Type Y: boosting
RMSE: 16.1822882172608
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
Model Type No: boosting
RMSE: 10.8598848190415
Params: nrounds: 100.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625
ATE (Yes-No): -14.888 (Std.Error: 2.875)
Trimmed ATE (Yes-No): -15.078 (Std.Error: 3.055)
Upper ATE (Yes-No): -10.407 (Std.Error: 6.831)
Observational differences in treatment -1.222 (Yes-No)
treatment outcome
1: Yes 12.21660
2: No 13.43816`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 6W. RPV
Distribution:
0% 25% 50% 75% 100%
-13.80090 -2.20050 2.16740 8.20375 35.50390
Model Type Y: boosting
RMSE: 10.9302660594733
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
Model Type No: boosting
RMSE: 7.1611855014717
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5
ATE (Yes-No): 4.229 (Std.Error: 2.892)
Trimmed ATE (Yes-No): 4.24 (Std.Error: 2.973)
Upper ATE (Yes-No): 3.885 (Std.Error: 3.908)
Observational differences in treatment 3.489 (Yes-No)
treatment outcome
1: Yes -4.402080
2: No -7.891561`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. RPV
Distribution:
0% 25% 50% 75% 100%
-22.18000 -1.24995 2.33515 6.49505 26.63460
Model Type Y: boosting
RMSE: 10.5280489479363
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5
Model Type No: boosting
RMSE: 6.36555163732614
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): 8.801 (Std.Error: 2.046)
Trimmed ATE (Yes-No): 8.946 (Std.Error: 2.108)
Upper ATE (Yes-No): 4.777 (Std.Error: 3.694)
Observational differences in treatment 1.581 (Yes-No)
treatment outcome
1: Yes -6.798891
2: No -8.379701`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 6W. RLL
Distribution:
0% 25% 50% 75% 100%
-50.4092 -0.1871 9.2750 24.0000 71.0000
Model Type Y: boosting
RMSE: 19.5253607043171
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
Model Type No: boosting
RMSE: 15.698189980176
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5
ATE (Yes-No): 5.41 (Std.Error: 4.537)
Trimmed ATE (Yes-No): 5.604 (Std.Error: 4.691)
Upper ATE (Yes-No): -0.02 (Std.Error: 5.694)
Observational differences in treatment 2.532 (Yes-No)
treatment outcome
1: Yes -11.99869
2: No -14.53073`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. RLL
Distribution:
0% 25% 50% 75% 100%
-22.5800 -0.8950 7.4295 24.4364 67.7026
Model Type Y: boosting
RMSE: 19.1146388615057
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75
Model Type No: boosting
RMSE: 15.0103300181806
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5
ATE (Yes-No): 11.646 (Std.Error: 4.853)
Trimmed ATE (Yes-No): 11.747 (Std.Error: 5.077)
Upper ATE (Yes-No): 9.105 (Std.Error: 7.061)
Observational differences in treatment 0.506 (Yes-No)
treatment outcome
1: Yes -14.51151
2: No -15.01760`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'